Automatic　segmentation　of　multiple　organs　is　a　challenging　topic.　Most　existing　approaches　are　based　on　2D　network　or　3D　network,　which　leads　to　insufficient　contextual　exploration　in　organ　segmentation.　In　recent　years,　many　methods　for　automatic　segmentation　based　on　fully　supervised　deep　learning　have　been　proposed.　However,　it　is　very　expensive　and　time-consuming　for　experienced　medical　practitioners　to　annotate　a　large　number　of　pixels.　In　this　paper,　we　propose　a　new　two-dimensional　multi　slices　semi-supervised　method　to　perform　the　task　of　abdominal　organ　segmentation.　The　network　adopts　the　information　along　the　z-axis　direction　in　CT　images,　preserves　and　exploits　the　useful　temporal　information　in　adjacent　slices.　Besides,　we　combine　Cross-Entropy　Loss　and　Dice　Loss　as　loss　functions　to　improve　the　performance　of　our　method.　We　apply　a　teacher-student　model　with　Exponential　Moving　Average　(EMA)　strategy　to　leverage　the　unlabeled　data.　The　student　model　is　trained　with　labeled　data,　and　the　teacher　model　is　obtained　by　smoothing　the　student　model　weights　via　EMA.　The　pseudo-labels　of　unlabeled　images　predicted　by　the　teacher　model　are　used　to　train　the　student　model　as　the　final　model.　The　mean　DSC　for　all　cases　we　obtained　on　the　validation　set　was　0.5684,　the　mean　NSD　was　0.5971,　and　the　total　run　time　was　783.14 s.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.